Benigson: information filter can help law firms add value
In the latest of our profiles on lawtech start-ups, Dan Bindman investigates Signal Media, which uses machine learning to train algorithms to ensure high-quality information that law firms can then use to enhance their efficiency and add value to clients.
A start-up using artificial intelligence (AI) to filter news and information, offering services that include being able to track how firms are perceived in the media and also give them intelligence tailored to their clients’ businesses, has acquired several major practices as clients.
Ultimately the company hopes to be able to use ‘big data’ to give firms’ clients an advance look at trends that may affect their businesses, either as risks or opportunities.
Signal Media, which has spent more than three years building machine learning components that make sense of vast amount of information, said it has Allen & Overy, DLA Piper and Mishcon de Reya on its books, among other big legal names.
London-based Signal’s website said: “We gather all the information the world had to offer…. From broadcast and radio channels, to print newspapers, right through to online media, legislation and regulatory documents. Then we pump all of it into the Signal system, the moment it is published…
“Signal’s AI reads, analyses and classifies it in seconds. Natural language processing automatically identifies all relevant information – from the names of companies mentioned, to the topic being discussed – instantly.”
Signal has raised more than $10m (£8m) in three rounds of investment, most recently almost $6m in December, and includes Reed Elsevier Ventures among investors. It has claimed to use technology to combat so-called ‘fake news’ by partnering with reputable news publishers such as Dow Jones and Reuters.
Law graduate and chief executive since he co-founded Signal in 2013, David Benigson, told Legal Futures that “a suite of upwards of 15 machine learning AI-driven components interrogate the information to try to make sense of it” by training algorithms to recognise patterns within the data.
He continued: “Essentially the machine learning is able to look at thousands and thousands of different variables at any given time to create a unique fingerprint of what that subject might look like… to make assumptions based on vast amounts of information and look at things that a human might not be aware of, such as grammar and the language that is used.”
Law firm clients were interested in three things in particular, he said, all involving sifting data to give them a business advantage: Firstly reputation management; namely “the ability for law firms to be able to track how they are perceived in the wider media – enabling them to track in real-time what people say about the firm and the context in which they are spoken about”.
Secondly, enabling a law firm to “get smart” on their clients in order to provide an enhanced, tailored, service: “To be able to say something that is insightful and meaningful and adds value to that client beyond the technical service delivery – knowing what’s going on in their world, having the context, being able to pull that into a digestible format that you can then integrate into your conversation.”
Lastly, said Mr Benigson, a “use case which is more nascent and embryonic for us”; using big data to discern trends in advance of changes in a client’s industry.
He explained: “As we begin to aggregate all the world’s legislation and regulation and begin to correlate that with the public information… we believe we can begin to provide not only alerting and analysis of that regulation to keep clients up-to-date with everything that is changing in a fast moving and global environment, but also begin to look at what we call the ‘regulation lifecycle’.”
This meant enabling “law firm clients to get ahead of… potential risks and opportunities by giving them access not only to the regulation, legislation, and case law but also the conversations around changes within those areas”.
He gave the example of payment protection insurance, problems with which he said were “spoken about online… before any new regulation was written and certainly before the big fines were issued to the banks”. Advance notice of this sort of developing issue could be advantageous to businesses operating in that space, he suggested.
He accepted that attempts would be made to manipulate news to achieve desired outcomes and to defeat the algorithms Signal used. But he said: “It’s always going to be a… race to ensure that the technology is evolving to keep up with what is a fast-moving landscape of information overload…
“We are thinking about how can we apply this very smart, nascent technology to solving those issues and enabling people to feel confident in the data that they are consuming.”
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